Evidence map›Paper›PMID 32372978›Full record

ArticleFrontiers in psychiatry2020

Proteomic Profiling as a Diagnostic Biomarker for Discriminating Between Bipolar and Unipolar Depression.

Sarah Kittel-Schneider, Tim Hahn, Frieder Haenisch, Rhiannon McNeill, Andreas Reif, Sabine Bahn

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sarah Kittel-SchneiderDepartment of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital, University of Würzburg, Würzburg, Germany.
Tim HahnDepartment of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital, University of Münster, Münster, Germany.
Frieder HaenischDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.
Rhiannon McNeillDepartment of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Hospital, University of Würzburg, Würzburg, Germany.
Andreas ReifDepartment of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe-University of Frankfurt, Frankfurt, Germany.
Sabine BahnDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAffective disorders are a major global burden, with approximately 15% of people worldwide suffering from some form of affective disorder. In patients experiencing their first depressive episode, in most cases it cannot be distinguished whether this is due to bipolar disorder (BD) or major depressive disorder (MDD). Valid fluid biomarkers able to discriminate between the two disorders in a clinical setting are not yet available. MATERIAL AND

methodsSeventy depressed patients suffering from BD (bipolar I and II subtypes) and 42 patients with major MDD were recruited and blood samples were taken for proteomic analyses after 8 h fasting. Proteomic profiles were analyzed using the Multiplex Immunoassay platform from Myriad Rules Based Medicine (Myriad RBM; Austin, Texas, USA). Human DiscoveryMAP

resultsBased on the various proteomic profiles, the algorithm could discriminate depressed BD patients from MDD patients with an accuracy of 67%. DISCUSSION: The results of this preliminary study suggest that future discrimination between bipolar and unipolar depression in a single case could be possible, using predictive biomarker models based on blood proteomic profiling.

Indexed as

affective disorderbiomarkerbipolar disorderbloodmachine learningmajor depression (MD)major depressive disorder (MDD)proteome

Identifiers

PMID32372978
PMCPMC7184109

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.